Overview

Dataset statistics

Number of variables24
Number of observations14993
Missing cells1278
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory192.0 B

Variable types

Categorical9
Text4
Numeric11

Alerts

Breed1 is highly overall correlated with TypeHigh correlation
Dewormed is highly overall correlated with VaccinatedHigh correlation
Gender is highly overall correlated with QuantityHigh correlation
Quantity is highly overall correlated with GenderHigh correlation
Type is highly overall correlated with Breed1High correlation
Vaccinated is highly overall correlated with DewormedHigh correlation
Health is highly imbalanced (85.6%)Imbalance
Name has 1265 (8.4%) missing valuesMissing
PetID has unique valuesUnique
Age has 179 (1.2%) zerosZeros
Breed2 has 10762 (71.8%) zerosZeros
Color2 has 4471 (29.8%) zerosZeros
Color3 has 10604 (70.7%) zerosZeros
Fee has 12663 (84.5%) zerosZeros
VideoAmt has 14419 (96.2%) zerosZeros
PhotoAmt has 341 (2.3%) zerosZeros

Reproduction

Analysis started2024-11-17 21:27:09.780996
Analysis finished2024-11-17 21:27:27.098161
Duration17.32 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
1
8132 
2
6861 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14993
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8132
54.2%
2 6861
45.8%

Length

2024-11-17T21:27:27.200738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T21:27:27.320761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 8132
54.2%
2 6861
45.8%

Most occurring characters

ValueCountFrequency (%)
1 8132
54.2%
2 6861
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8132
54.2%
2 6861
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8132
54.2%
2 6861
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8132
54.2%
2 6861
45.8%

Name
Text

MISSING 

Distinct9059
Distinct (%)66.0%
Missing1265
Missing (%)8.4%
Memory size117.3 KiB
2024-11-17T21:27:28.019327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length47
Median length42
Mean length9.5142045
Min length1

Characters and Unicode

Total characters130611
Distinct characters170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7808 ?
Unique (%)56.9%

Sample

1st rowNibble
2nd rowNo Name Yet
3rd rowBrisco
4th rowMiko
5th rowHunter
ValueCountFrequency (%)
1062
 
4.3%
and 437
 
1.8%
kittens 318
 
1.3%
puppies 298
 
1.2%
kitten 274
 
1.1%
for 259
 
1.0%
adoption 214
 
0.9%
puppy 206
 
0.8%
boy 193
 
0.8%
2 184
 
0.7%
Other values (6908) 21401
86.1%
2024-11-17T21:27:28.858974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11187
 
8.6%
e 10582
 
8.1%
i 8792
 
6.7%
a 7766
 
5.9%
o 7729
 
5.9%
n 6128
 
4.7%
t 5209
 
4.0%
r 4627
 
3.5%
l 4624
 
3.5%
y 4244
 
3.2%
Other values (160) 59723
45.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11187
 
8.6%
e 10582
 
8.1%
i 8792
 
6.7%
a 7766
 
5.9%
o 7729
 
5.9%
n 6128
 
4.7%
t 5209
 
4.0%
r 4627
 
3.5%
l 4624
 
3.5%
y 4244
 
3.2%
Other values (160) 59723
45.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11187
 
8.6%
e 10582
 
8.1%
i 8792
 
6.7%
a 7766
 
5.9%
o 7729
 
5.9%
n 6128
 
4.7%
t 5209
 
4.0%
r 4627
 
3.5%
l 4624
 
3.5%
y 4244
 
3.2%
Other values (160) 59723
45.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11187
 
8.6%
e 10582
 
8.1%
i 8792
 
6.7%
a 7766
 
5.9%
o 7729
 
5.9%
n 6128
 
4.7%
t 5209
 
4.0%
r 4627
 
3.5%
l 4624
 
3.5%
y 4244
 
3.2%
Other values (160) 59723
45.7%

Age
Real number (ℝ)

ZEROS 

Distinct106
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.452078
Minimum0
Maximum255
Zeros179
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:29.241641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q312
95-th percentile48
Maximum255
Range255
Interquartile range (IQR)10

Descriptive statistics

Standard deviation18.15579
Coefficient of variation (CV)1.7370509
Kurtosis20.772138
Mean10.452078
Median Absolute Deviation (MAD)2
Skewness3.7629749
Sum156708
Variance329.63273
MonotonicityNot monotonic
2024-11-17T21:27:29.412712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3503
23.4%
1 2304
15.4%
3 1966
13.1%
4 1109
 
7.4%
12 967
 
6.4%
24 651
 
4.3%
5 595
 
4.0%
6 558
 
3.7%
36 417
 
2.8%
8 309
 
2.1%
Other values (96) 2614
17.4%
ValueCountFrequency (%)
0 179
 
1.2%
1 2304
15.4%
2 3503
23.4%
3 1966
13.1%
4 1109
 
7.4%
5 595
 
4.0%
6 558
 
3.7%
7 281
 
1.9%
8 309
 
2.1%
9 184
 
1.2%
ValueCountFrequency (%)
255 2
 
< 0.1%
238 1
 
< 0.1%
212 3
 
< 0.1%
180 2
 
< 0.1%
168 1
 
< 0.1%
156 1
 
< 0.1%
147 1
 
< 0.1%
144 4
< 0.1%
135 1
 
< 0.1%
132 8
0.1%

Breed1
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.27259
Minimum0
Maximum307
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:29.566387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile109
Q1265
median266
Q3307
95-th percentile307
Maximum307
Range307
Interquartile range (IQR)42

Descriptive statistics

Standard deviation60.056818
Coefficient of variation (CV)0.22639662
Kurtosis4.8419545
Mean265.27259
Median Absolute Deviation (MAD)41
Skewness-2.2209198
Sum3977232
Variance3606.8214
MonotonicityNot monotonic
2024-11-17T21:27:29.771139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
307 5927
39.5%
266 3634
24.2%
265 1258
 
8.4%
299 342
 
2.3%
264 296
 
2.0%
292 264
 
1.8%
285 221
 
1.5%
141 205
 
1.4%
205 190
 
1.3%
179 167
 
1.1%
Other values (166) 2489
16.6%
ValueCountFrequency (%)
0 5
< 0.1%
1 2
 
< 0.1%
3 1
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
10 3
 
< 0.1%
11 2
 
< 0.1%
15 9
0.1%
16 1
 
< 0.1%
17 6
< 0.1%
ValueCountFrequency (%)
307 5927
39.5%
306 56
 
0.4%
305 8
 
0.1%
304 7
 
< 0.1%
303 42
 
0.3%
302 1
 
< 0.1%
301 5
 
< 0.1%
300 21
 
0.1%
299 342
 
2.3%
298 1
 
< 0.1%

Breed2
Real number (ℝ)

ZEROS 

Distinct135
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.009738
Minimum0
Maximum307
Zeros10762
Zeros (%)71.8%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:30.002517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3179
95-th percentile307
Maximum307
Range307
Interquartile range (IQR)179

Descriptive statistics

Standard deviation123.01157
Coefficient of variation (CV)1.6620999
Kurtosis-0.6045826
Mean74.009738
Median Absolute Deviation (MAD)0
Skewness1.1374997
Sum1109628
Variance15131.848
MonotonicityNot monotonic
2024-11-17T21:27:30.239193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10762
71.8%
307 1727
 
11.5%
266 599
 
4.0%
265 321
 
2.1%
299 138
 
0.9%
264 125
 
0.8%
292 105
 
0.7%
218 91
 
0.6%
141 86
 
0.6%
285 78
 
0.5%
Other values (125) 961
 
6.4%
ValueCountFrequency (%)
0 10762
71.8%
1 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
10 2
 
< 0.1%
14 2
 
< 0.1%
16 2
 
< 0.1%
17 1
 
< 0.1%
18 3
 
< 0.1%
ValueCountFrequency (%)
307 1727
11.5%
306 24
 
0.2%
305 6
 
< 0.1%
304 1
 
< 0.1%
303 24
 
0.2%
302 2
 
< 0.1%
301 1
 
< 0.1%
300 9
 
0.1%
299 138
 
0.9%
296 3
 
< 0.1%

Gender
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
2
7277 
1
5536 
3
2180 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14993
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 7277
48.5%
1 5536
36.9%
3 2180
 
14.5%

Length

2024-11-17T21:27:30.383014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T21:27:30.498896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 7277
48.5%
1 5536
36.9%
3 2180
 
14.5%

Most occurring characters

ValueCountFrequency (%)
2 7277
48.5%
1 5536
36.9%
3 2180
 
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 7277
48.5%
1 5536
36.9%
3 2180
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 7277
48.5%
1 5536
36.9%
3 2180
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 7277
48.5%
1 5536
36.9%
3 2180
 
14.5%

Color1
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2341759
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:30.601725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7452254
Coefficient of variation (CV)0.78114948
Kurtosis1.0067426
Mean2.2341759
Median Absolute Deviation (MAD)1
Skewness1.4726056
Sum33497
Variance3.0458116
MonotonicityNot monotonic
2024-11-17T21:27:30.716079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 7427
49.5%
2 3750
25.0%
3 947
 
6.3%
5 884
 
5.9%
6 684
 
4.6%
7 667
 
4.4%
4 634
 
4.2%
ValueCountFrequency (%)
1 7427
49.5%
2 3750
25.0%
3 947
 
6.3%
4 634
 
4.2%
5 884
 
5.9%
6 684
 
4.6%
7 667
 
4.4%
ValueCountFrequency (%)
7 667
 
4.4%
6 684
 
4.6%
5 884
 
5.9%
4 634
 
4.2%
3 947
 
6.3%
2 3750
25.0%
1 7427
49.5%

Color2
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2228373
Minimum0
Maximum7
Zeros4471
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:30.826708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.7425617
Coefficient of variation (CV)0.8509774
Kurtosis-1.5093342
Mean3.2228373
Median Absolute Deviation (MAD)2
Skewness0.19095217
Sum48320
Variance7.5216449
MonotonicityNot monotonic
2024-11-17T21:27:30.937818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4471
29.8%
7 3438
22.9%
2 3313
22.1%
5 1128
 
7.5%
6 1063
 
7.1%
4 870
 
5.8%
3 710
 
4.7%
ValueCountFrequency (%)
0 4471
29.8%
2 3313
22.1%
3 710
 
4.7%
4 870
 
5.8%
5 1128
 
7.5%
6 1063
 
7.1%
7 3438
22.9%
ValueCountFrequency (%)
7 3438
22.9%
6 1063
 
7.1%
5 1128
 
7.5%
4 870
 
5.8%
3 710
 
4.7%
2 3313
22.1%
0 4471
29.8%

Color3
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8820116
Minimum0
Maximum7
Zeros10604
Zeros (%)70.7%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:31.045561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9840858
Coefficient of variation (CV)1.5855831
Kurtosis-0.90066465
Mean1.8820116
Median Absolute Deviation (MAD)0
Skewness1.009939
Sum28217
Variance8.9047678
MonotonicityNot monotonic
2024-11-17T21:27:31.155970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 10604
70.7%
7 3221
 
21.5%
5 417
 
2.8%
6 378
 
2.5%
4 198
 
1.3%
3 175
 
1.2%
ValueCountFrequency (%)
0 10604
70.7%
3 175
 
1.2%
4 198
 
1.3%
5 417
 
2.8%
6 378
 
2.5%
7 3221
 
21.5%
ValueCountFrequency (%)
7 3221
 
21.5%
6 378
 
2.5%
5 417
 
2.8%
4 198
 
1.3%
3 175
 
1.2%
0 10604
70.7%

MaturitySize
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
2
10305 
1
3395 
3
1260 
4
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14993
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 10305
68.7%
1 3395
 
22.6%
3 1260
 
8.4%
4 33
 
0.2%

Length

2024-11-17T21:27:31.281573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T21:27:31.396096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 10305
68.7%
1 3395
 
22.6%
3 1260
 
8.4%
4 33
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 10305
68.7%
1 3395
 
22.6%
3 1260
 
8.4%
4 33
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 10305
68.7%
1 3395
 
22.6%
3 1260
 
8.4%
4 33
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 10305
68.7%
1 3395
 
22.6%
3 1260
 
8.4%
4 33
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 10305
68.7%
1 3395
 
22.6%
3 1260
 
8.4%
4 33
 
0.2%

FurLength
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
1
8808 
2
5361 
3
 
824

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14993
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8808
58.7%
2 5361
35.8%
3 824
 
5.5%

Length

2024-11-17T21:27:31.516763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T21:27:31.628261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 8808
58.7%
2 5361
35.8%
3 824
 
5.5%

Most occurring characters

ValueCountFrequency (%)
1 8808
58.7%
2 5361
35.8%
3 824
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8808
58.7%
2 5361
35.8%
3 824
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8808
58.7%
2 5361
35.8%
3 824
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8808
58.7%
2 5361
35.8%
3 824
 
5.5%

Vaccinated
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
2
7227 
1
5898 
3
1868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14993
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 7227
48.2%
1 5898
39.3%
3 1868
 
12.5%

Length

2024-11-17T21:27:31.752781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T21:27:31.865904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 7227
48.2%
1 5898
39.3%
3 1868
 
12.5%

Most occurring characters

ValueCountFrequency (%)
2 7227
48.2%
1 5898
39.3%
3 1868
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 7227
48.2%
1 5898
39.3%
3 1868
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 7227
48.2%
1 5898
39.3%
3 1868
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 7227
48.2%
1 5898
39.3%
3 1868
 
12.5%

Dewormed
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
1
8397 
2
4815 
3
1781 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14993
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 8397
56.0%
2 4815
32.1%
3 1781
 
11.9%

Length

2024-11-17T21:27:32.002104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T21:27:32.114669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 8397
56.0%
2 4815
32.1%
3 1781
 
11.9%

Most occurring characters

ValueCountFrequency (%)
1 8397
56.0%
2 4815
32.1%
3 1781
 
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8397
56.0%
2 4815
32.1%
3 1781
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8397
56.0%
2 4815
32.1%
3 1781
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8397
56.0%
2 4815
32.1%
3 1781
 
11.9%

Sterilized
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
2
10077 
1
3101 
3
1815 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14993
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 10077
67.2%
1 3101
 
20.7%
3 1815
 
12.1%

Length

2024-11-17T21:27:32.232520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T21:27:32.345853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 10077
67.2%
1 3101
 
20.7%
3 1815
 
12.1%

Most occurring characters

ValueCountFrequency (%)
2 10077
67.2%
1 3101
 
20.7%
3 1815
 
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 10077
67.2%
1 3101
 
20.7%
3 1815
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 10077
67.2%
1 3101
 
20.7%
3 1815
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 10077
67.2%
1 3101
 
20.7%
3 1815
 
12.1%

Health
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
1
14478 
2
 
481
3
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14993
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 14478
96.6%
2 481
 
3.2%
3 34
 
0.2%

Length

2024-11-17T21:27:32.466151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T21:27:32.578097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 14478
96.6%
2 481
 
3.2%
3 34
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 14478
96.6%
2 481
 
3.2%
3 34
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 14478
96.6%
2 481
 
3.2%
3 34
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 14478
96.6%
2 481
 
3.2%
3 34
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 14478
96.6%
2 481
 
3.2%
3 34
 
0.2%

Quantity
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5760688
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:32.686423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum20
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4724773
Coefficient of variation (CV)0.93427217
Kurtosis34.086775
Mean1.5760688
Median Absolute Deviation (MAD)0
Skewness4.5998197
Sum23630
Variance2.1681893
MonotonicityNot monotonic
2024-11-17T21:27:32.815881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 11565
77.1%
2 1422
 
9.5%
3 726
 
4.8%
4 531
 
3.5%
5 333
 
2.2%
6 185
 
1.2%
7 84
 
0.6%
8 52
 
0.3%
9 33
 
0.2%
10 19
 
0.1%
Other values (9) 43
 
0.3%
ValueCountFrequency (%)
1 11565
77.1%
2 1422
 
9.5%
3 726
 
4.8%
4 531
 
3.5%
5 333
 
2.2%
6 185
 
1.2%
7 84
 
0.6%
8 52
 
0.3%
9 33
 
0.2%
10 19
 
0.1%
ValueCountFrequency (%)
20 12
0.1%
18 1
 
< 0.1%
17 3
 
< 0.1%
16 3
 
< 0.1%
15 4
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 6
 
< 0.1%
11 10
0.1%
10 19
0.1%

Fee
Real number (ℝ)

ZEROS 

Distinct74
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.259988
Minimum0
Maximum3000
Zeros12663
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:32.969963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile150
Maximum3000
Range3000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation78.414548
Coefficient of variation (CV)3.6883627
Kurtosis191.70064
Mean21.259988
Median Absolute Deviation (MAD)0
Skewness8.9213849
Sum318751
Variance6148.8413
MonotonicityNot monotonic
2024-11-17T21:27:33.119558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12663
84.5%
50 468
 
3.1%
100 408
 
2.7%
200 219
 
1.5%
150 162
 
1.1%
20 136
 
0.9%
300 120
 
0.8%
30 103
 
0.7%
250 92
 
0.6%
1 82
 
0.5%
Other values (64) 540
 
3.6%
ValueCountFrequency (%)
0 12663
84.5%
1 82
 
0.5%
2 1
 
< 0.1%
5 24
 
0.2%
8 7
 
< 0.1%
9 5
 
< 0.1%
10 70
 
0.5%
14 1
 
< 0.1%
15 20
 
0.1%
20 136
 
0.9%
ValueCountFrequency (%)
3000 1
 
< 0.1%
2000 1
 
< 0.1%
1000 4
 
< 0.1%
800 2
 
< 0.1%
750 7
< 0.1%
700 5
 
< 0.1%
688 1
 
< 0.1%
650 4
 
< 0.1%
600 13
0.1%
599 1
 
< 0.1%

State
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41346.028
Minimum41324
Maximum41415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:33.243436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum41324
5-th percentile41326
Q141326
median41326
Q341401
95-th percentile41401
Maximum41415
Range91
Interquartile range (IQR)75

Descriptive statistics

Standard deviation32.444153
Coefficient of variation (CV)0.00078469817
Kurtosis-0.7847167
Mean41346.028
Median Absolute Deviation (MAD)0
Skewness1.0911148
Sum6.19901 × 108
Variance1052.6231
MonotonicityNot monotonic
2024-11-17T21:27:33.351133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
41326 8714
58.1%
41401 3845
25.6%
41327 843
 
5.6%
41336 507
 
3.4%
41330 420
 
2.8%
41332 253
 
1.7%
41324 137
 
0.9%
41325 110
 
0.7%
41335 85
 
0.6%
41361 26
 
0.2%
Other values (4) 53
 
0.4%
ValueCountFrequency (%)
41324 137
 
0.9%
41325 110
 
0.7%
41326 8714
58.1%
41327 843
 
5.6%
41330 420
 
2.8%
41332 253
 
1.7%
41335 85
 
0.6%
41336 507
 
3.4%
41342 13
 
0.1%
41345 22
 
0.1%
ValueCountFrequency (%)
41415 3
 
< 0.1%
41401 3845
25.6%
41367 15
 
0.1%
41361 26
 
0.2%
41345 22
 
0.1%
41342 13
 
0.1%
41336 507
 
3.4%
41335 85
 
0.6%
41332 253
 
1.7%
41330 420
 
2.8%
Distinct5595
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:33.792412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters479776
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3783 ?
Unique (%)25.2%

Sample

1st row8480853f516546f6cf33aa88cd76c379
2nd row3082c7125d8fb66f7dd4bff4192c8b14
3rd rowfa90fa5b1ee11c86938398b60abc32cb
4th row9238e4f44c71a75282e62f7136c6b240
5th row95481e953f8aed9ec3d16fc4509537e8
ValueCountFrequency (%)
fa90fa5b1ee11c86938398b60abc32cb 459
 
3.1%
aa66486163b6cbc25ea62a34b11c9b91 315
 
2.1%
c00756f2bdd8fa88fc9f07a8309f7d5d 231
 
1.5%
b53c34474d9e24574bcec6a3d3306a0d 228
 
1.5%
ee2747ce26468ec44c7194e7d1d9dad9 156
 
1.0%
95481e953f8aed9ec3d16fc4509537e8 134
 
0.9%
b770bac0ca797cf1433c48a35d30c4cb 111
 
0.7%
a042471e0f43f2cf707104a1a138a7df 95
 
0.6%
fd970cc91d06d82eebf046340137b272 93
 
0.6%
7ed6d84e2e6879245e55447aee39c328 85
 
0.6%
Other values (5585) 13086
87.3%
2024-11-17T21:27:34.519238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 31917
 
6.7%
3 31504
 
6.6%
4 31313
 
6.5%
7 30408
 
6.3%
e 29991
 
6.3%
1 29933
 
6.2%
a 29865
 
6.2%
6 29860
 
6.2%
9 29842
 
6.2%
8 29821
 
6.2%
Other values (6) 175322
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 479776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 31917
 
6.7%
3 31504
 
6.6%
4 31313
 
6.5%
7 30408
 
6.3%
e 29991
 
6.3%
1 29933
 
6.2%
a 29865
 
6.2%
6 29860
 
6.2%
9 29842
 
6.2%
8 29821
 
6.2%
Other values (6) 175322
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 479776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 31917
 
6.7%
3 31504
 
6.6%
4 31313
 
6.5%
7 30408
 
6.3%
e 29991
 
6.3%
1 29933
 
6.2%
a 29865
 
6.2%
6 29860
 
6.2%
9 29842
 
6.2%
8 29821
 
6.2%
Other values (6) 175322
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 479776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 31917
 
6.7%
3 31504
 
6.6%
4 31313
 
6.5%
7 30408
 
6.3%
e 29991
 
6.3%
1 29933
 
6.2%
a 29865
 
6.2%
6 29860
 
6.2%
9 29842
 
6.2%
8 29821
 
6.2%
Other values (6) 175322
36.5%

VideoAmt
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.056759821
Minimum0
Maximum8
Zeros14419
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:34.652778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34618455
Coefficient of variation (CV)6.0991128
Kurtosis124.42463
Mean0.056759821
Median Absolute Deviation (MAD)0
Skewness9.4585332
Sum851
Variance0.11984374
MonotonicityNot monotonic
2024-11-17T21:27:34.769194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 14419
96.2%
1 417
 
2.8%
2 92
 
0.6%
3 36
 
0.2%
4 15
 
0.1%
5 7
 
< 0.1%
6 4
 
< 0.1%
8 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 14419
96.2%
1 417
 
2.8%
2 92
 
0.6%
3 36
 
0.2%
4 15
 
0.1%
5 7
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 1
 
< 0.1%
6 4
 
< 0.1%
5 7
 
< 0.1%
4 15
 
0.1%
3 36
 
0.2%
2 92
 
0.6%
1 417
 
2.8%
0 14419
96.2%
Distinct14031
Distinct (%)93.7%
Missing13
Missing (%)0.1%
Memory size117.3 KiB
2024-11-17T21:27:35.449762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6664
Median length1474
Mean length339.60754
Min length1

Characters and Unicode

Total characters5087321
Distinct characters1571
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13709 ?
Unique (%)91.5%

Sample

1st rowNibble is a 3+ month old ball of cuteness. He is energetic and playful. I rescued a couple of cats a few months ago but could not get them neutered in time as the clinic was fully scheduled. The result was this little kitty. I do not have enough space and funds to care for more cats in my household. Looking for responsible people to take over Nibble's care.
2nd rowI just found it alone yesterday near my apartment. It was shaking so I had to bring it home to provide temporary care.
3rd rowTheir pregnant mother was dumped by her irresponsible owner at the roadside near some shops in Subang Jaya. Gave birth to them at the roadside. They are all healthy and adorable puppies. Already dewormed, vaccinated and ready to go to a home. No tying or caging for long hours as guard dogs. However, it is acceptable to cage or tie for precautionary purposes. Interested to adopt pls call me.
4th rowGood guard dog, very alert, active, obedience waiting for her good master, plz call or sms for more details if you really get interested, thanks!!
5th rowThis handsome yet cute boy is up for adoption. He is the most playful pal we've seen in our puppies. He loves to nibble on shoelaces , Chase you at such a young age. Imagine what a cute brat he will be when he grows. We are looking for a loving home for Hunter , one that will take care of him and give him the love that he needs. Please call urgently if you would like to adopt this cutie.
ValueCountFrequency (%)
and 31297
 
3.3%
to 29340
 
3.1%
a 24359
 
2.6%
the 18661
 
2.0%
is 18362
 
1.9%
for 14731
 
1.6%
13581
 
1.4%
i 11131
 
1.2%
her 10753
 
1.1%
she 10231
 
1.1%
Other values (28145) 761417
80.7%
2024-11-17T21:27:36.337384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
929199
18.3%
e 484184
 
9.5%
a 334407
 
6.6%
o 311112
 
6.1%
t 304793
 
6.0%
n 256217
 
5.0%
i 239530
 
4.7%
r 214949
 
4.2%
s 207639
 
4.1%
h 177624
 
3.5%
Other values (1561) 1627667
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5087321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
929199
18.3%
e 484184
 
9.5%
a 334407
 
6.6%
o 311112
 
6.1%
t 304793
 
6.0%
n 256217
 
5.0%
i 239530
 
4.7%
r 214949
 
4.2%
s 207639
 
4.1%
h 177624
 
3.5%
Other values (1561) 1627667
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5087321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
929199
18.3%
e 484184
 
9.5%
a 334407
 
6.6%
o 311112
 
6.1%
t 304793
 
6.0%
n 256217
 
5.0%
i 239530
 
4.7%
r 214949
 
4.2%
s 207639
 
4.1%
h 177624
 
3.5%
Other values (1561) 1627667
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5087321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
929199
18.3%
e 484184
 
9.5%
a 334407
 
6.6%
o 311112
 
6.1%
t 304793
 
6.0%
n 256217
 
5.0%
i 239530
 
4.7%
r 214949
 
4.2%
s 207639
 
4.1%
h 177624
 
3.5%
Other values (1561) 1627667
32.0%

PetID
Text

UNIQUE 

Distinct14993
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:36.879162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters134937
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14993 ?
Unique (%)100.0%

Sample

1st row86e1089a3
2nd row6296e909a
3rd row3422e4906
4th row5842f1ff5
5th row850a43f90
ValueCountFrequency (%)
86e1089a3 1
 
< 0.1%
aaedd873d 1
 
< 0.1%
7843a9dca 1
 
< 0.1%
efbf1703a 1
 
< 0.1%
3422e4906 1
 
< 0.1%
5842f1ff5 1
 
< 0.1%
850a43f90 1
 
< 0.1%
d24c30b4b 1
 
< 0.1%
1caa6fcdb 1
 
< 0.1%
97aa9eeac 1
 
< 0.1%
Other values (14983) 14983
99.9%
2024-11-17T21:27:37.589724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 8622
 
6.4%
a 8555
 
6.3%
8 8533
 
6.3%
d 8526
 
6.3%
f 8522
 
6.3%
1 8476
 
6.3%
3 8454
 
6.3%
0 8439
 
6.3%
6 8434
 
6.3%
b 8431
 
6.2%
Other values (6) 49945
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134937
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 8622
 
6.4%
a 8555
 
6.3%
8 8533
 
6.3%
d 8526
 
6.3%
f 8522
 
6.3%
1 8476
 
6.3%
3 8454
 
6.3%
0 8439
 
6.3%
6 8434
 
6.3%
b 8431
 
6.2%
Other values (6) 49945
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134937
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 8622
 
6.4%
a 8555
 
6.3%
8 8533
 
6.3%
d 8526
 
6.3%
f 8522
 
6.3%
1 8476
 
6.3%
3 8454
 
6.3%
0 8439
 
6.3%
6 8434
 
6.3%
b 8431
 
6.2%
Other values (6) 49945
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134937
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 8622
 
6.4%
a 8555
 
6.3%
8 8533
 
6.3%
d 8526
 
6.3%
f 8522
 
6.3%
1 8476
 
6.3%
3 8454
 
6.3%
0 8439
 
6.3%
6 8434
 
6.3%
b 8431
 
6.2%
Other values (6) 49945
37.0%

PhotoAmt
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.889215
Minimum0
Maximum30
Zeros341
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2024-11-17T21:27:37.731817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile10
Maximum30
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4878102
Coefficient of variation (CV)0.8967903
Kurtosis12.645918
Mean3.889215
Median Absolute Deviation (MAD)2
Skewness2.860638
Sum58311
Variance12.16482
MonotonicityNot monotonic
2024-11-17T21:27:37.855793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 3075
20.5%
2 2518
16.8%
3 2511
16.7%
5 2147
14.3%
4 1881
12.5%
6 621
 
4.1%
7 432
 
2.9%
0 341
 
2.3%
8 314
 
2.1%
9 231
 
1.5%
Other values (21) 922
 
6.1%
ValueCountFrequency (%)
0 341
 
2.3%
1 3075
20.5%
2 2518
16.8%
3 2511
16.7%
4 1881
12.5%
5 2147
14.3%
6 621
 
4.1%
7 432
 
2.9%
8 314
 
2.1%
9 231
 
1.5%
ValueCountFrequency (%)
30 19
0.1%
29 6
 
< 0.1%
28 7
 
< 0.1%
27 6
 
< 0.1%
26 10
0.1%
25 8
0.1%
24 15
0.1%
23 12
0.1%
22 9
0.1%
21 16
0.1%

AdoptionSpeed
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
4
4197 
2
4037 
3
3259 
1
3090 
0
 
410

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14993
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
4 4197
28.0%
2 4037
26.9%
3 3259
21.7%
1 3090
20.6%
0 410
 
2.7%

Length

2024-11-17T21:27:37.991298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T21:27:38.106725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 4197
28.0%
2 4037
26.9%
3 3259
21.7%
1 3090
20.6%
0 410
 
2.7%

Most occurring characters

ValueCountFrequency (%)
4 4197
28.0%
2 4037
26.9%
3 3259
21.7%
1 3090
20.6%
0 410
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4197
28.0%
2 4037
26.9%
3 3259
21.7%
1 3090
20.6%
0 410
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4197
28.0%
2 4037
26.9%
3 3259
21.7%
1 3090
20.6%
0 410
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4197
28.0%
2 4037
26.9%
3 3259
21.7%
1 3090
20.6%
0 410
 
2.7%

Interactions

2024-11-17T21:27:24.885182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:12.727358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:13.897078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:15.128266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:16.309203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:17.449478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:18.602554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:19.945318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:21.145292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:22.326344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:23.686774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:24.985896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:12.867827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:13.992100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:15.225424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:16.405357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:17.548833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:18.862064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:20.052506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:21.244209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:22.448180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:23.787012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:25.092326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:12.969183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:14.198000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:15.322684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:16.506027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:17.659179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:18.969918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:20.163203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:21.346879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:22.560850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:23.893756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:25.198734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:13.068009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:14.296999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:15.425826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:16.609871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:17.764529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:19.077312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:20.272619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:21.449935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:22.681407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:24.001857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:25.326380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:13.170745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:14.403894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:15.532193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:16.713744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:17.867953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:19.191640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:20.377690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:21.552856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:22.781732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:24.108993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:25.436025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:13.273960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:14.511726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:15.652411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:16.817239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:17.970873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:19.300170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:20.486962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:21.668723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:22.891262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:24.216226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:25.559814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:13.375432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:14.616206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:15.757748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:16.924484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:18.077801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:19.405974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:20.600440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:21.783100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:23.015240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:24.326555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:25.696960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:13.484222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:14.723475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:15.872438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:17.040599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:18.184656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:19.517455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:20.710072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:21.897956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:23.122996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:24.437672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:25.838443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:13.585506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:14.825413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:15.985556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:17.140053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:18.289803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:19.623008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:20.820165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:22.002493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:23.228434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:24.552570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:25.961875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:13.681528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:14.921384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:16.097146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:17.235196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:18.385470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:19.724569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:20.922291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:22.101411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:23.319637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:24.651879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:26.113732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:13.794574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:15.029007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:16.208573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:17.348304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:18.498055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:19.834517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:21.036718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:22.221771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:23.583911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T21:27:24.776792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-11-17T21:27:38.225498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AdoptionSpeedAgeBreed1Breed2Color1Color2Color3DewormedFeeFurLengthGenderHealthMaturitySizePhotoAmtQuantityStateSterilizedTypeVaccinatedVideoAmt
AdoptionSpeed1.0000.2100.150-0.016-0.031-0.036-0.0040.0680.0180.0750.0480.0240.056-0.0620.0530.0340.1620.1030.096-0.011
Age0.2101.000-0.251-0.0220.158-0.037-0.0860.1290.1130.1170.0760.0710.093-0.101-0.2400.0420.2120.1570.188-0.031
Breed10.150-0.2511.000-0.123-0.071-0.111-0.0900.150-0.1300.1690.1020.0310.205-0.0040.037-0.0360.1140.8450.2100.025
Breed2-0.016-0.022-0.1231.000-0.031-0.0020.0280.0710.0340.1170.0820.0270.0730.0690.061-0.0560.0660.4120.099-0.009
Color1-0.0310.158-0.071-0.0311.000-0.099-0.3070.0640.0600.0680.1230.0190.046-0.049-0.1570.0180.0600.2920.081-0.004
Color2-0.036-0.037-0.111-0.002-0.0991.0000.1090.074-0.0040.0410.1890.0170.0570.0850.0640.0170.0480.3540.0860.020
Color3-0.004-0.086-0.0900.028-0.3070.1091.0000.075-0.0100.0150.2080.0120.0420.1110.3080.0090.0350.2280.0780.017
Dewormed0.0680.1290.1500.0710.0640.0740.0751.000-0.1250.0470.1560.0640.081-0.0960.1680.0130.4500.1580.747-0.033
Fee0.0180.113-0.1300.0340.060-0.004-0.010-0.1251.0000.1140.0250.0000.055-0.004-0.080-0.0240.0250.0500.0650.009
FurLength0.0750.1170.1690.1170.0680.0410.0150.0470.1141.0000.0270.0270.113-0.019-0.044-0.0370.0510.0460.058-0.018
Gender0.0480.0760.1020.0820.1230.1890.2080.1560.0250.0271.0000.0300.0780.0850.5440.0120.0930.1220.1380.006
Health0.0240.0710.0310.0270.0190.0170.0120.0640.0000.0270.0301.0000.044-0.029-0.0400.0200.0550.0000.068-0.005
MaturitySize0.0560.0930.2050.0730.0460.0570.0420.0810.0550.1130.0780.0441.0000.008-0.063-0.0660.0750.1970.0980.011
PhotoAmt-0.062-0.101-0.0040.069-0.0490.0850.111-0.096-0.004-0.0190.085-0.0290.0081.0000.148-0.0240.0610.0710.0600.153
Quantity0.053-0.2400.0370.061-0.1570.0640.3080.168-0.080-0.0440.544-0.040-0.0630.1481.0000.0310.0980.1110.145-0.000
State0.0340.042-0.036-0.0560.0180.0170.0090.013-0.024-0.0370.0120.020-0.066-0.0240.0311.0000.0340.1340.034-0.027
Sterilized0.1620.2120.1140.0660.0600.0480.0350.4500.0250.0510.0930.0550.0750.0610.0980.0341.0000.0840.492-0.015
Type0.1030.1570.8450.4120.2920.3540.2280.1580.0500.0460.1220.0000.1970.0710.1110.1340.0841.0000.2410.001
Vaccinated0.0960.1880.2100.0990.0810.0860.0780.7470.0650.0580.1380.0680.0980.0600.1450.0340.4920.2411.000-0.027
VideoAmt-0.011-0.0310.025-0.009-0.0040.0200.017-0.0330.009-0.0180.006-0.0050.0110.153-0.000-0.027-0.0150.001-0.0271.000

Missing values

2024-11-17T21:27:26.315403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-17T21:27:26.719723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-17T21:27:27.008495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TypeNameAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateRescuerIDVideoAmtDescriptionPetIDPhotoAmtAdoptionSpeed
02Nibble3299011701122211100413268480853f516546f6cf33aa88cd76c3790Nibble is a 3+ month old ball of cuteness. He is energetic and playful. I rescued a couple of cats a few months ago but could not get them neutered in time as the clinic was fully scheduled. The result was this little kitty. I do not have enough space and funds to care for more cats in my household. Looking for responsible people to take over Nibble's care.86e1089a31.02
12No Name Yet12650112022333110414013082c7125d8fb66f7dd4bff4192c8b140I just found it alone yesterday near my apartment. It was shaking so I had to bring it home to provide temporary care.6296e909a2.00
21Brisco1307012702211211041326fa90fa5b1ee11c86938398b60abc32cb0Their pregnant mother was dumped by her irresponsible owner at the roadside near some shops in Subang Jaya. Gave birth to them at the roadside. They are all healthy and adorable puppies. Already dewormed, vaccinated and ready to go to a home. No tying or caging for long hours as guard dogs. However, it is acceptable to cage or tie for precautionary purposes. Interested to adopt pls call me.3422e49067.03
31Miko4307021202111211150414019238e4f44c71a75282e62f7136c6b2400Good guard dog, very alert, active, obedience waiting for her good master, plz call or sms for more details if you really get interested, thanks!!5842f1ff58.02
41Hunter130701100212221104132695481e953f8aed9ec3d16fc4509537e80This handsome yet cute boy is up for adoption. He is the most playful pal we've seen in our puppies. He loves to nibble on shoelaces , Chase you at such a young age. Imagine what a cute brat he will be when he grows. We are looking for a loving home for Hunter , one that will take care of him and give him the love that he needs. Please call urgently if you would like to adopt this cutie.850a43f903.02
52NaN326602560212221104132622fe332bf9c924d4718005891c63fbed0This is a stray kitten that came to my house. Have been feeding it, but cannot keep it.d24c30b4b2.02
62BULAT1226426411002322311300413261e0b5a458b5b77f5af581d57ebf570b30anyone within the area of ipoh or taiping who interested to adopt my cat can contact my father at this number (mazuvil)or can just email me. currently bulat is at my hometown at perak but anyone outside the area still want to adopt can travel there to my hometown.there is a lot of cats in my house rite now..i think i should let one of them go to a better owner who can give better attention to him.1caa6fcdb3.01
71Siu Pak & Her 6 Puppies03070212721222160413261fba5f6e5480946254590d48f9c5198d0Siu Pak just give birth on 13/6/10 to 6puppies. Interested pls call or sms me. Left 2female puppies on 2/7/1097aa9eeac9.03
82NaN2265026002222211041326d8af7afece71334473575c9f70daf00d0healthy and active, feisty kitten found in neighbours' garden. Not sure of sex.c06d167ca6.01
92Kitty122650217022333110413261f3f36e4b18e94855b3e88af0852fdc40Very manja and gentle stray cat found, we would really like to find a home for it because we cannot keep her for ourselves for long. Has a very cute high pitch but soft meow. Please contact me if you would be interested in adopting.7a0942d612.04
TypeNameAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateRescuerIDVideoAmtDescriptionPetIDPhotoAmtAdoptionSpeed
149831Alger3307011272211211041326fa90fa5b1ee11c86938398b60abc32cb0He is very intelligent and cute. Fluffy and looks much better in real life than in the photo. He deserves a good home. No tying or caging for long hours except for precautionary purposes Serious adopter pls callcca88204d7.02
149841NaN60307022502233311041324c8ea0bc42e630c72747986c4c0ce36aa0abandoned,but healthyf5dc70d351.04
149851Terry2417930712372233211041326719987dce7aeb027fdfa91b4808001990been at my place for a while..am hoping to find it a good homee7f7066b60.04
149862Pets + Strays : BlueEyed BlackWhite126602567212121104140190569c3f7cb0af35cba5dac82c0ac9d701 month old white + grey kitten for adoption near HUKM, KL, near Bdr Tun Razak Gender / medical record + costs To Be Confirmed. Adopter MUST commit to NEUTER kitten when it is : * 4-6 months old * on heat whichever comes first, provided that it is * 1.4 kg weight MINIMUM Whatsapp for adopption / FREE gift / startup kit / Sign up contract to buy supplies from Pets + Strays, comes with FREE gifts whilst stocks last36e7f8d831.03
149871Snowy619502170131121104140179309f4027f2fedb4349a298c69fe56f0ooooo4d163b7311.00
149882NaN226603100222221404132661c84bd7bcb6fb31d2d480b1bcf9682e0I have 4 kittens that need to be adopt urgently. It about 1 1/2 months old. My cat got pregnant before we got the chance to get its muted. The kittens are healthy and are eating kittens biscuits now. They are very playful and love being pat I prefer the kittens to be going to the same home but I do understands and its can be adopt separately. I'm hopping the kittens will get a lovely home soondc0935a843.02
149892Serato & Eddie60265264314722111120413261d5096c4a5e159a3b750c5cfcf6ceabf0Serato(female cat- 3 color) is 4 years old and Eddie(male cat- white and cream) is 1 years plus. Both are toilet train and can't be separated. Needs a loving home together.a01ab5b303.04
149902Monkies22652663567322131530413266f40a7acfad5cc0bb3e44591ea446c050Mix breed, good temperament kittens. Love humans. Very friendly.d981b63955.03
149912Ms Daym9266024701111111041336c311c0c569245baa147d91fa4e351ae40she is very shy..adventures and independent..she just hates cages..but loves climbing trees and rooftops..however she is very loving.e4da1c9e43.04
149921Fili1307307120021222110413329ed1d5493d223eaa5024c1a031dbc9c20Fili just loves laying around and also loves being under the sun; Very laidback and quiet.a83d95ead1.03